Classifying purchasing behavior
## Loading required package: nlme
## Warning: package 'nlme' was built under R version 3.6.2
## This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.
## Classes 'tbl_df', 'tbl' and 'data.frame': 1779 obs. of 8 variables:
## $ purchase : num 0 0 0 0 0 0 0 0 1 0 ...
## $ n_acts : num 11 0 6 8 8 1 5 0 9 18 ...
## $ bal_crdt_ratio : num 0 36.1 17.6 12.5 59.1 ...
## $ avg_prem_balance : num 2494 2494 2494 2494 2494 ...
## $ retail_crdt_ratio: num 0 11.5 0 0.8 20.8 ...
## $ avg_fin_balance : num 1767 1767 0 1021 797 ...
## $ mortgage_age : num 182 139 139 139 93 ...
## $ cred_limit : num 12500 0 0 0 0 0 0 0 11500 16000 ...
## (Intercept) s(mortgage_age).1 s(mortgage_age).2 s(mortgage_age).3
## 0.2072950 0.5606807 0.7590564 0.5339771
## s(mortgage_age).4 s(mortgage_age).5 s(mortgage_age).6 s(mortgage_age).7
## 0.6313451 0.4315829 0.4079339 0.5625195
## s(mortgage_age).8 s(mortgage_age).9
## 0.9634169 0.3712867
Purchase behavior with multiple smooths
##
## Family: binomial
## Link function: logit
##
## Formula:
## purchase ~ s(n_acts) + s(bal_crdt_ratio) + s(avg_prem_balance) +
## s(retail_crdt_ratio) + s(avg_fin_balance) + s(mortgage_age) +
## s(cred_limit)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.64060 0.07557 -21.71 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(n_acts) 3.474 4.310 93.670 < 2e-16 ***
## s(bal_crdt_ratio) 4.308 5.257 18.386 0.00318 **
## s(avg_prem_balance) 2.275 2.816 7.800 0.04958 *
## s(retail_crdt_ratio) 1.001 1.001 1.422 0.23343
## s(avg_fin_balance) 1.850 2.202 2.506 0.27895
## s(mortgage_age) 4.669 5.710 9.656 0.13401
## s(cred_limit) 1.001 1.002 23.066 1.58e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.184 Deviance explained = 18.4%
## -REML = 781.37 Scale est. = 1 n = 1779
Visualising influences on purchasing probability




Predicting purchasing behavior and uncertainty
## $fit
## 1 2 3 4 5 6 7
## -1.301155 0.772986 -2.913542 -2.913542 -2.263154 -1.277517 -1.339294
##
## $se.fit
## 1 2 3 4 5 6 7
## 0.2157911 0.3149660 0.1646090 0.1646090 0.1770119 0.3924700 0.1786229
## 1 2 3 4 5 6 7
## 0.25249228 0.74799587 0.06014694 0.06014694 0.11045101 0.29213299 0.23854530
## 1 2 3 4 5 6 7
## 0.17991165 0.61254436 0.04401756 0.04401756 0.08016065 0.15842590 0.17976842
Explaining individual behaviors
## s(n_acts) s(bal_crdt_ratio) s(avg_prem_balance) s(retail_crdt_ratio)
## 1 -0.3626621 0.33525205 0.36950597 -0.007532527
## 2 1.8221395 0.36655349 0.07893092 0.067899489
## 3 -0.8850186 -0.40588190 -0.13592053 -0.007532527
## 4 -0.8850186 -0.40588190 -0.13592053 -0.007532527
## 5 -0.6228781 -0.01763481 -0.13592053 -0.007532527
## 6 0.5693622 -0.41095678 0.32942053 0.067899489
## 7 -0.1078251 0.39117226 -0.13592053 -0.007532527
## s(avg_fin_balance) s(mortgage_age) s(cred_limit)
## 1 -0.04057249 -0.17744840 0.2229033
## 2 0.13456166 0.19538844 -0.2518867
## 3 -0.04057249 -0.02091843 0.2229033
## 4 -0.04057249 -0.02091843 0.2229033
## 5 -0.04057249 -0.02091843 0.2229033
## 6 0.15606041 0.08355615 -0.4322582
## 7 -0.04057249 -0.02091843 0.2229033
## attr(,"constant")
## (Intercept)
## -1.640601